Fujitsu is not a parasite for continuing to profit from government contracts in the wake of the Post Office Horizon scandal, its boss told MPs. European chief executive Paul Patterson said Fujitsu had been given £500m of contract extensions despite its faulty software being at the centre of the huge miscarriage of justice. We are not a parasite, the government has got an option as to whether they wish to extend those contracts or not, he said, adding it would not bid for new business. Patterson also repeatedly refused to say how much Fujitsu would contribute to the £1.8bn redress scheme for victims of the scandal, currently funded by taxpayers. More than 900 sub-postmasters were prosecuted after the faulty Horizon computer system made it look like money was missing from their branch accounts.

Victims of the Horizon Post Office scandal could meet face-to-face with Fujitsu and Post Office representatives as part of a restorative justice effort. The charity overseeing a new scheme said the first five months were an initial pilot phase, but it hoped the scheme would last five years and include extra initiatives such as a special commemorative postage stamp. It comes on top of the various financial compensation schemes in place for sub-postmasters. The Horizon IT scandal saw hundreds of sub-postmasters falsely accused of embezzling Post Office funds after faulty software suggested money was missing from their branch accounts. Children affected by Post Office scandal'should get grants' More than 900 sub-postmasters were wrongly prosecuted because of incorrect information from the Horizon computer system.

It sometimes happens that a random British TV show will suddenly shoot to enormous, worldwide acclaim without a big publicity campaign to push it there, instead driven primarily by word of mouth. The best example of this is 2024's Baby Reindeer, which became a hit and sparked real-life twists and turns to rival those within the series itself. The latest example, Adolescence, has seen success on a different scale, though. The four-part drama, about a 13-year-old boy named Jamie who is arrested for murdering a girl at his school, became one of Netflix's most popular series of all time--beating out Stranger Things Season 3--within just the first 17 days of its release. Why is everyone watching this show?

This study explores text-to-SQL parsing by leveraging the powerful reasoning capabilities of large language models (LLMs). Despite recent advancements, existing LLM-based methods have not adequately addressed scalability, leading to inefficiencies when processing wide tables. Furthermore, current interaction-based approaches either lack a step-by-step, interpretable SQL generation process or fail to provide an efficient and universally applicable interaction design. To address these challenges, we introduce Interactive-T2S, a framework that generates SQL queries through direct interactions with databases. This framework includes four general tools that facilitate proactive and efficient information retrieval by the LLM. Additionally, we have developed detailed exemplars to demonstrate the step-wise reasoning processes within our framework. Our experiments on the BIRD-Dev dataset, employing a setting without oracle knowledge, reveal that our method achieves state-of-the-art results with only two exemplars, underscoring the effectiveness and robustness of our framework.

Theory of Mind (ToM) refers to the ability to attribute mental states, such as beliefs, desires, intentions, and knowledge, to oneself and others, and to understand that these mental states can differ from one's own and from reality. We investigate ToM in environments with multiple, distinct, independent AI agents, each possessing unique internal states, information, and objectives. Inspired by human false-belief experiments, we present an AI ('focal AI') with a scenario where its clone undergoes a human-centric ToM assessment. We prompt the focal AI to assess whether its clone would benefit from additional instructions. Concurrently, we give its clones the ToM assessment, both with and without the instructions, thereby engaging the focal AI in higher-order counterfactual reasoning akin to human mentalizing--with respect to humans in one test and to other AI in another. We uncover a discrepancy: Contemporary AI demonstrates near-perfect accuracy on human-centric ToM assessments. Since information embedded in one AI is identically embedded in its clone, additional instructions are redundant. Yet, we observe AI crafting elaborate instructions for their clones, erroneously anticipating a need for assistance. An independent referee AI agrees with these unsupported expectations. Neither the focal AI nor the referee demonstrates ToM in our 'silico-centric' test.

As a tax professor, I love taxes: the theory, the policy, even the politics. But I have a confession to make. My taxes are not complicated. Yet, every year, I spend hour upon hour gathering documents, paying for tax preparation software, entering in my income, and puzzling through the instructions as I try to figure out whether I am eligible for this or that deduction or credit. Every year, I think to myself: There has got to be a better way!

Ministers need to "immediately" update the law to acknowledge that computers are fallible or risk a repeat of the Horizon scandal, legal experts say. In English and Welsh law, computers are assumed to be "reliable" unless proven otherwise. But critics of this approach say this reverses the burden of proof normally applied in criminal cases. Stephen Mason, a barrister and expert on electronic evidence, said: "It says, for the person who's saying'there's something wrong with this computer', that they have to prove it. Even if it's the person accusing them who has the information."

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The former head of Britain's state-owned Post Office said Tuesday she will hand back a royal honor in response to mounting fury over a miscarriage of justice that saw hundreds of postmasters wrongfully accused of theft because of a faulty computer system. The British government is considering whether to offer a mass amnesty to more than 700 branch managers convicted of theft or fraud between 1999 and 2015, because Post Office computers wrongly showed that money was missing from their shops. The real culprit was a defective accounting system called Horizon, supplied by the Japanese technology firm Fujitsu.

Current natural language systems designed for multi-step claim validation typically operate in two phases: retrieve a set of relevant premise statements using heuristics (planning), then generate novel conclusions from those statements using a large language model (deduction). The planning step often requires expensive Transformer operations and does not scale to arbitrary numbers of premise statements. In this paper, we investigate whether an efficient planning heuristic is possible via embedding spaces compatible with deductive reasoning. Specifically, we evaluate whether embedding spaces exhibit a property we call deductive additivity: the sum of premise statement embeddings should be close to embeddings of conclusions based on those premises. We explore multiple sources of off-the-shelf dense embeddings in addition to fine-tuned embeddings from GPT3 and sparse embeddings from BM25. We study embedding models both intrinsically, evaluating whether the property of deductive additivity holds, and extrinsically, using them to assist planning in natural language proof generation. Lastly, we create a dataset, Single-Step Reasoning Contrast (SSRC), to further probe performance on various reasoning types. Our findings suggest that while standard embedding methods frequently embed conclusions near the sums of their premises, they fall short of being effective heuristics and lack the ability to model certain categories of reasoning.

Congress should pass reform legislation that would establish a Technology Innovation Fund for the U.S. Postal Service (USPS) to enable robotic last-mile postal delivery, a new report states. "Of particular promise are sorting and delivery robots, which could sort mail, including into local delivery orders, deliver mail to homes, or both," according to "A New Vision for Postal Reform in the E-commerce Age," a Feb. 11 report from the Information Technology and Innovation Foundation (ITIF). "One could imagine a postal worker driving to particular routes with a fleet of 10 or so robots, letting each one off to'walk' a particular mail route, and then picking them back up at the end of the route." This funding would help support innovation at USPS, the report states, likening the approach to those at the Defense Department and NASA, which get federal funding for automation and robotics research. Although robotics is not sophisticated or inexpensive enough yet to sort and deliver mail, progress is happening.